class_docstr: We just have this in case the input data is keyed by string
class_docstr_tok: ["We","just","have","this","in","case","the","input","data","is","keyed","by","string"]
code: def process(
      self, sparse_data: List[Dict[str, float]]
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      # Convert all keys to integers
      sparse_data_int = []
      for sd in sparse_data:
          sd_int = {}
          for k, v in sd.items():
              sd_int[int(k)] = v
          sparse_data_int.append(sd_int)
      return self._sparse_to_dense(sparse_data_int)
  
file_path: ReAgent/reagent/preprocessing/sparse_to_dense.py
imports: from collections import defaultdict
  from typing import Dict, List, Tuple
  import torch
  from reagent.preprocessing import normalization
label_desc: Use image node_id 2 for calling the StringKeySparseToDenseProcessor obj's underlying member method code with example usage: obj.process(sparse_data) and returns: self